AlgoTradeKit 0.4.1

Algorithmic Trading Toolkit — data collection, indicators, strategy, simulation, and live trading

AlgoTradeKit

Algorithmic Trading Toolkit — collect market data, build indicators and strategies, backtest, and trade live.

pip install AlgoTradeKit

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Modules

Module	Status	Description
data	✅ v0.1.0	Collect OHLCV candles from exchanges
indicator	✅ v0.4.0	RSI, MACD, MA family, Ichimoku — TradingView-compatible
visual	✅ v0.3.0	Candlestick charts, indicators, live streaming
strategy	🔜 planned	Build and combine trading strategies
simulate	🔜 planned	Backtest strategies on historical data
trade	🔜 planned	Live trading via exchange API or MT5

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Quick Start

Collect candle data

from AlgoTradeKit.data import Collector

collector = Collector(source="binance-futures", symbol="BTCUSDT", timeframe="1d")
collector.destination = "data/"
collector.starttime   = "2020/01/01"
collector.collect()

Compute indicators

import pandas as pd
from AlgoTradeKit.indicator import RSI, MACD, EMA, SMA, Ichimoku

df = pd.read_csv("data/binance-futures_BTCUSDT_1d.csv")

# RSI (default: length=14, OB=70, OS=30)
rsi = RSI(df["close"])
print(rsi.rsi.tail())

# RSI with MA overlay
rsi_ma = RSI(df["close"], length=14, show_ma=True, ma_type="EMA", ma_length=9)

# MACD (default: 12, 26, 9)
macd = MACD(df["close"])
print(macd.macd.tail(), macd.signal.tail(), macd.histogram.tail())

# EMA
ema20 = EMA(df["close"], length=20)

# Ichimoku (all default TradingView settings)
ichi = Ichimoku(df["high"], df["low"], df["close"])
print(ichi.tenkan.tail())
print(ichi.cloud_df().tail(30))  # includes 26 future bars

Visualise with indicators

from AlgoTradeKit.visual import Chart, add_rsi, add_macd, add_ma, add_ichimoku
from AlgoTradeKit.indicator import RSI, MACD, EMA, Ichimoku

chart = Chart.from_csv("data/binance-futures_BTCUSDT_1d.csv")

rsi  = RSI(chart.df["close"])
macd = MACD(chart.df["close"])
ema  = EMA(chart.df["close"], length=20)
ichi = Ichimoku(chart.df["high"], chart.df["low"], chart.df["close"])

add_ma(chart,       ema,  timestamps=chart.df["timestamp"])
add_ichimoku(chart, ichi, timestamps=chart.df["timestamp"])
add_rsi(chart,      rsi,  timestamps=chart.df["timestamp"])
add_macd(chart,     macd, timestamps=chart.df["timestamp"])

chart.show()   # opens browser at http://localhost:9000

Feature	Details
Ichimoku cloud	Canvas-drawn fill between Span A/B — bullish teal / bearish red
Indicator grouping	Multi-line indicators grouped with collapsible header, hide-all, remove-all
Legend position	Top-left of chart pane
Settings ⚙	Font, axis label toggles (last close / indicator prices on right axis)
Cursor axis label	Always shows candle close price, never indicator values

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Data Module

Collect candle data

from AlgoTradeKit.data import Collector

collector = Collector(source="binance-spot", symbol="ETHUSDT", timeframe="4h")
collector.destination = "data/"
collector.starttime   = "2021/01/01"
collector.endtime     = "2023/01/01"   # optional — defaults to now
collector.collect()                     # returns path to saved CSV

Resample timeframes

from AlgoTradeKit.data import Converter

conv = Converter(source="data/binance-futures_BTCUSDT_1h.csv", target_timeframe="4h")
conv.destination = "data/"
conv.convert()

Supported sources

Source key	Market
"binance-spot"	Binance Spot
"binance-futures"	Binance USD-M Futures

Supported timeframes

1m 3m 5m 15m 30m 1h 2h 4h 6h 8h 12h 1d 3d 1w 1M

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Indicator Reference

MA Family

All Moving Averages accept source (price series) and length (period).

Class	Formula	TV Default	TV Colour
SMA	Rolling mean	9	#2962FF
EMA	α = 2/(n+1)	9	#FF6D00
WMA	Linear weights	9	#00BCD4
SMMA	Wilder RMA, α = 1/n	9	#4CAF50
DEMA	2·EMA − EMA(EMA)	9	#F44336
TEMA	3·EMA − 3·EMA(EMA) + EMA(EMA(EMA))	9	#FF9800
HullMA	WMA(2·WMA(n/2)−WMA(n), √n)	9	#9C27B0
VWMA	Σ(price·vol) / Σ(vol)	20	#E040FB
VWAP	Cumulative PV/V with σ bands	—	#2962FF

from AlgoTradeKit.indicator import SMA, EMA, WMA, VWMA, SMMA, DEMA, TEMA, HullMA, VWAP

sma  = SMA(close, length=20)
ema  = EMA(close, length=20)
wma  = WMA(close, length=20)
vwma = VWMA(close, volume, length=20)
smma = SMMA(close, length=20)
dema = DEMA(close, length=20)
tema = TEMA(close, length=20)
hma  = HullMA(close, length=20)
vwap = VWAP(high, low, close, volume, anchor="none", bands=[1, 2])

RSI

rsi = RSI(
    source      = df["close"],
    length      = 14,          # period (TV default)
    overbought  = 70,          # upper level (TV default)
    oversold    = 30,          # lower level (TV default)
    show_ma     = False,       # add MA on RSI line
    ma_type     = "EMA",       # EMA | SMA | SMMA | WMA
    ma_length   = 14,
)

rsi.rsi                    # RSI series
rsi.is_overbought()        # bool series
rsi.is_oversold()          # bool series
rsi.crossover_ob()         # entered overbought
rsi.crossunder_os()        # exited oversold (bullish signal)

MACD

macd = MACD(
    source        = df["close"],
    fast_length   = 12,        # TV default
    slow_length   = 26,        # TV default
    signal_length = 9,         # TV default
    oscillator_ma = "EMA",     # EMA | SMA | SMMA | WMA
    signal_ma     = "EMA",     # EMA | SMA | SMMA | WMA
)

macd.macd                  # MACD line
macd.signal                # Signal line
macd.histogram             # MACD − Signal
macd.histogram_colors()    # per-bar TV 4-colour Series
macd.crossover()           # bullish MACD/signal cross
macd.zero_crossover()      # MACD crosses above zero

Ichimoku

ichi = Ichimoku(
    high            = df["high"],
    low             = df["low"],
    close           = df["close"],
    tenkan_period   = 9,        # TV default
    kijun_period    = 26,       # TV default
    senkou_b_period = 52,       # TV default
    displacement    = 26,       # TV default
)

ichi.tenkan                # Conversion Line
ichi.kijun                 # Base Line
ichi.senkou_a              # Leading Span A  (26 bars forward)
ichi.senkou_b              # Leading Span B  (26 bars forward)
ichi.chikou                # Lagging Span    (26 bars back)
ichi.cloud_df()            # DataFrame with full cloud + 26 future bars
ichi.tk_cross_bullish()    # Tenkan crosses above Kijun
ichi.price_above_cloud()   # Close above both Span A and B

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Installation

Requires Python 3.10+

pip install AlgoTradeKit

For development:

git clone https://github.com/AmirMohammadBazdar/AlgoTradeKit.git
cd AlgoTradeKit
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

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Requirements

• pandas >= 2.0
• requests >= 2.28
• fastapi >= 0.110.0 (visual module)
• uvicorn >= 0.29.0 (visual module)
• websockets >= 12.0 (visual module)

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Roadmap

• data module — Collector (Binance Spot & Futures)
• data module — Converter (timeframe resampling)
• visual module — interactive candlestick chart
• indicator module — RSI, MACD, MA family, Ichimoku
• indicator — Bollinger Bands, ATR, Stochastic, VWAP session anchor
• strategy module — strategy builder with entry/exit logic
• simulate module — backtesting engine with full report
• trade module — live trading via exchange API / MT5
• MEXC, Bybit, OKX data sources

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License

MIT — see LICENSE